Experiment 8.1

Time: 2016-04-15 16:00:28

Commit: 0e5134c2af678ddd626cce6e44d98086a9089981

Parameters

learningRate : 0.3

randomJump : 1

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 300

numberOfAgents : 150

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 8.2

Time: 2016-04-15 17:12:13

Commit: ff447f5b291c44063eec67a197b19afdba504578

Parameters

learningRate : 0.5

randomJump : 1

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 300

numberOfAgents : 150

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

</div>

</body> </html>

Experiment 7.1

Time: 2016-04-14 16:50:29

Commit: 0883dc6cbc273500d66c67227c6143182413fa55

Parameters

learningRate : 0.3

randomJump : 1

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.2

Time: 2016-04-14 19:24:49

Commit: 159e2da6c706aa2588955bc033b7aea7dc1e9ecc

Parameters

learningRate : 0.1

randomJump : 99

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.3

Time: 2016-04-14 20:31:15

Commit: 17d8194b65477a2c95a69d5fffa6491fcc391d82

Parameters

learningRate : 0.1

randomJump : 50

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.4

Time: 2016-04-14 18:16:04

Commit: 186e78a5ad721baac67496a49fe1e6c146f197bc

Parameters

learningRate : 0.1

randomJump : 1

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.5

Time: 2016-04-14 14:53:37

Commit: 26c5cbd7b38ec4baaf89a13cd2bdc217d6a0b836

Parameters

learningRate : 0.1

randomJump : 1

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.6

Time: 2016-04-14 15:28:44

Commit: 27ce18d5d7dac9cf5cf2d4be2c6da3376c598f16

Parameters

learningRate : 0.1

randomJump : 50

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.7

Time: 2016-04-14 19:41:51

Commit: 3226c69f03155d7baaf16718d63db25874ad70d6

Parameters

learningRate : 0.3

randomJump : 99

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.8

Time: 2016-04-14 15:58:20

Commit: 37e6f3861beadb8ea49161a5e16b9335cdab29ed

Parameters

learningRate : 0.1

randomJump : 99

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.9

Time: 2016-04-14 18:50:20

Commit: 3b875d850fb0e8dd7985a098dd7b13553139a070

Parameters

learningRate : 0.1

randomJump : 50

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.10

Time: 2016-04-14 16:14:45

Commit: 5f60873edb355644532dacc21f93dd2fb39b38ea

Parameters

learningRate : 0.3

randomJump : 99

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.11

Time: 2016-04-14 20:48:18

Commit: 7a59ad17fdf061d8ddaced90f71631e987e54524

Parameters

learningRate : 0.3

randomJump : 50

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.12

Time: 2016-04-14 15:41:25

Commit: 7d91669c2cbb094bab697f49bf4342c4a1a60476

Parameters

learningRate : 0.3

randomJump : 50

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.13

Time: 2016-04-14 21:23:09

Commit: 8a13707ad3055fad270c65049f3900b16d6a3009

Parameters

learningRate : 0.3

randomJump : 99

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.14

Time: 2016-04-14 17:58:19

Commit: 8c1e25edfd60117b4b7cd7ea175eb78a5b89d414

Parameters

learningRate : 0.3

randomJump : 99

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.15

Time: 2016-04-14 15:10:30

Commit: 9cdc90324e6ac7072e64557230c1d6fd0e7483d6

Parameters

learningRate : 0.3

randomJump : 1

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.16

Time: 2016-04-14 21:05:01

Commit: a71d39875ba0c1ad5a6b95ddbb19b479119d3161

Parameters

learningRate : 0.1

randomJump : 99

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.17

Time: 2016-04-14 17:06:24

Commit: bffb1423ade37619d965b5e3c836010242a6ed2d

Parameters

learningRate : 0.1

randomJump : 50

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.18

Time: 2016-04-14 17:27:02

Commit: c5350ce3ad8fe6d21e6926665624583793452c25

Parameters

learningRate : 0.3

randomJump : 50

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.19

Time: 2016-04-14 18:32:37

Commit: d8fa57941f047b3cf30aa65287563e85e9e5ab2c

Parameters

learningRate : 0.3

randomJump : 1

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.20

Time: 2016-04-14 16:30:11

Commit: dbe54ab9506c7d64a302c18e292d5e299113fb43

Parameters

learningRate : 0.1

randomJump : 1

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.21

Time: 2016-04-14 17:40:14

Commit: e7b49c0a2e89b87aecacf1eb7353760aa994e81c

Parameters

learningRate : 0.1

randomJump : 99

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : true

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.22

Time: 2016-04-14 19:07:48

Commit: ead43d435f9a5354c2167a315724eac906e36adb

Parameters

learningRate : 0.3

randomJump : 50

penaltyCalculatedOnlyOnce : true

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.23

Time: 2016-04-14 19:59:24

Commit: ef53c75162f1a1894493a4c580316debd2281dcd

Parameters

learningRate : 0.1

randomJump : 1

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

Experiment 7.24

Time: 2016-04-14 20:15:54

Commit: f4277af13f988ec185ca5823f868e8e0e9682975

Parameters

learningRate : 0.3

randomJump : 1

penaltyCalculatedOnlyOnce : false

respectKnowsLinksWeights : false

believe : 0.15

numberOfCycles : 150

numberOfAgents : 100

bufferSize : 10

Benefit per unit of time

Benefit per unit of time

</div>

</body> </html>

Date: Thu Mar 17 15:37:54 2016
Analysis version (challprop-analytics): {analysis_commitNo}
Experiment version (challprop-experiments): 940d9ea7
Codebase version (challprop-java): c068ace4
(use git checkout $commit$ if needed)

Basic Statistics

These very roughly characterize what kind of data structure we get out of simulation.

## 
## NUMBER OF VERTIXES:
## situation=11250
## benefitHolder=72566
## situationInst=83816
## agent=500
## god=1
## 
## NUMBER OF EDGES:
## knowsLinks=6424
## challenged=207811
## interpreted=206966
## buffered=180683
## processed=72566
## reinterpreted=72566
## forgot=68411
## 
## PERFORMANCE:
## Number of atomic events: 809003
## Total simulation time (secs): 21950.4182055690
## Total simulation time (hours): 6.0973383904
## Average time per atomic event (secs): 0.0271326784

Dynamics of benefit

## 
## AVERAGE TOTAL BENEFIT:
## 14915.543698186015
## 
## AVERAGE BENEFIT:
## [:]

Average benefit

## Warning: closing unused connection 6 (code.version)

Average benefit change per unit of time

Degree distribution

## Total number of outDegrees:  6424

Full simulation parameters

## $pathOnDisk
## [1] "/media/data/challprop-working/eln/"
## 
## $generateTestDataOnDisk
## [1] "false"
## 
## $tempConfigFile
## [1] "/media/challprop-working/temp/challprop.conf"
## 
## $test
## [1] "false"
## 
## $graphDatabase
## [1] "TitanCassandraLocal"
## 
## $gephiVisualization
## [1] "false"
## 
## $useDatabaseOfVectors
## [1] "true"
## 
## $useDatabaseOfMatrixes
## [1] "true"
## 
## $penaltyCalculatedOnlyOnce
## [1] "true"
## 
## $rivalComponents
## [1] "[0, 1, 2, 3, 4]"
## 
## $cstSituation
## [1] "0.05"
## 
## $densityVectorSituation
## [1] "0.25"
## 
## $percnegSituation
## [1] "0.2"
## 
## $exponentSituation
## [1] "2"
## 
## $numberOfAgents
## [1] "500"
## 
## $decayFactor
## [1] "1"
## 
## $decayRate
## [1] "0.03"
## 
## $reciprocityRate
## [1] "0.2"
## 
## $linkToGodWeight
## [1] "1.0"
## 
## $weightImportance
## [1] "0.2"
## 
## $believe
## [1] "0.15"
## 
## $propagateRate
## [1] "0.4"
## 
## $dimensions
## [1] "10"
## 
## $densityMatrix
## [1] "0.5"
## 
## $rfactor
## [1] "0.5"
## 
## $numberOfCycles
## [1] "150"
## 
## $learningRate
## [1] "0.1"
## 
## $randomJump
## [1] "1"
## 
## $propagationThreshold
## [1] "0"
## 
## $disableRandomJump
## [1] "false"
## 
## $maxBranchingFactor
## [1] "1000"
## 
## $respectKnowsLinksWeights
## [1] "false"
## 
## $bufferSize
## [1] "10"
## 
## $ignoreNonRivalCorrection
## [1] "false"
## 
## $cstNeed
## [1] "0.05"
## 
## $densityVectorNeed
## [1] "0.5"
## 
## $percnegNeed
## [1] "0.5"
## 
## $exponentNeed
## [1] "2"
## 
## $simulationStartWall
## [1] "32572676938179"
## 
## $simulationStartCPU
## [1] "1291377944"
## 
## $simulationFinishWall
## [1] "54523095143748"
## 
## $simulationFinishCPU
## [1] "21095450486165"
## 
## $archivingFinishedWall
## [1] "54642774669714"
## 
## $archivingFinishedCPU
## [1] "21148847051074"
## 
## $deletingCassandraWall
## [1] "54642795209516"
## 
## $deletingCassandraCPU
## [1] "21148864190015"

Date: Sat Mar 12 14:06:09 2016
Analysis version (challprop-analytics): {analysis_commitNo}
Experiment version (challprop-experiments): 2911856a
Codebase version (challprop-java): 77c6efd7
(use git checkout $commit$ if needed)

Basic Statistics

These very roughly characterize what kind of data structure we get out of simulation.

##
## NUMBER OF VERTIXES:
## situation=11250
## benefitHolder=73063
## situationInst=84313
## agent=500
## god=1
##
## NUMBER OF EDGES:
## knowsLinks=8622
## challenged=360144
## interpreted=358608
## buffered=229504
## processed=73063
## reinterpreted=73063
## forgot=116725
##
## PERFORMANCE:
## Number of atomic events: 1211107
## Total simulation time (secs): 7797.3883380440
## Total simulation time (hours): 2.1659412050
## Average time per atomic event (secs): 0.0064382324

Dynamics of benefit

##
## AVERAGE TOTAL BENEFIT:
## 17533.421462008027
##
## AVERAGE BENEFIT:
## [:]

Average benefit

## Warning: closing unused connection 6 (code.version)

Average benefit change per unit of time

Degree distribution

## Total number of outDegrees:  8622

Full simulation parameters

## $pathOnDisk
## [1] "/media/data/challprop-working/eln/"
##
## $generateTestDataOnDisk
## [1] "false"
##
## $tempConfigFile
## [1] "/media/challprop-working/temp/challprop.conf"
##
## $test
## [1] "false"
##
## $graphDatabase
## [1] "TitanCassandraLocal"
##
## $gephiVisualization
## [1] "false"
##
## $useDatabaseOfVectors
## [1] "true"
##
## $useDatabaseOfMatrixes
## [1] "true"
##
## $rivalComponents
## [1] "[0, 1, 2, 3, 4]"
##
## $cstSituation
## [1] "0.05"
##
## $densityVectorSituation
## [1] "0.25"
##
## $percnegSituation
## [1] "0.2"
##
## $exponentSituation
## [1] "2"
##
## $numberOfAgents
## [1] "500"
##
## $decayFactor
## [1] "1"
##
## $decayRate
## [1] "0.03"
##
## $reciprocityRate
## [1] "0.2"
##
## $linkToGodWeight
## [1] "1.0"
##
## $weightImportance
## [1] "0.2"
##
## $believe
## [1] "0.15"
##
## $propagateRate
## [1] "0.4"
##
## $dimensions
## [1] "10"
##
## $densityMatrix
## [1] "0.5"
##
## $rfactor
## [1] "0.5"
##
## $numberOfCycles
## [1] "150"
##
## $newSituationsPerCycle
## [1] "5"
##
## $learningRate
## [1] "0.1"
##
## $randomJump
## [1] "1"
##
## $propagationThreshold
## [1] "0"
##
## $disableRandomJump
## [1] "false"
##
## $maxBranchingFactor
## [1] "1000"
##
## $respectKnowsLinksWeights
## [1] "false"
##
## $bufferSize
## [1] "10"
##
## $ignoreNonRivalCorrection
## [1] "false"
##
## $cstNeed
## [1] "0.05"
##
## $densityVectorNeed
## [1] "0.5"
##
## $percnegNeed
## [1] "0.5"
##
## $exponentNeed
## [1] "2"
##
## $simulationStartWall
## [1] "876386016069"
##
## $simulationStartCPU
## [1] "1197415854"
##
## $simulationFinishWall
## [1] "8673774354113"
##
## $simulationFinishCPU
## [1] "6714455343949"
##
## $archivingFinishedWall
## [1] "8957153230269"
##
## $archivingFinishedCPU
## [1] "6829430601420"
##
## $deletingCassandraWall
## [1] "8957167112575"
##
## $deletingCassandraCPU
## [1] "6829443668604"

Date: Wed Feb 11 18:12:00 2015
Analysis version (challprop-analytics): ee7d453
(use git checkout $commit$ if needed)

Back to the experiment page

First, the number of links between agents in the graph: 990. The graph is sparse (average degree 0.33) given that we have 3000 of agents. The average link weight is 8.12. 98% of all link weights are between -21 and 92:

##          1%         10%         20%         30%         50%         70% 
## -21.1730335  -2.3335033  -0.3338948   0.1175692   1.8546510   4.9825556 
##         90%         99% 
##  17.9730976  92.2649634

0.269697 of all links have negative or zero weight. A density plot of 98% of link weights (i.e. between 21 and 92) shows that they are more or less normally distributed:

This shows that most of the links are positive but very small

Back to the experiment page

Date: Wed Feb 11 17:29:52 2015
Analysis version (challprop-analytics): df5e83f
(use git checkout $commit$ if needed)

Back to the experiment page

The number of out degrees of each agent is the same as in degrees, because we create reciprocal link for every link that is being created between two agents. Therefore we account only for outDegrees. The chart below shows that most of the agents have 10-15 outDegrees, but there are a few agents that have up to 30 connections to other agents.

## Total number of outDegrees:  13364

Back to the experiment page

Date: Sat Dec 27 16:25:10 2014
Analysis version (challprop-analytics): 4cb1bdd
(use git checkout $commit$ if needed)

Back to the experiment page

The number of links between agents in the graph: 13364. The graph is sparse (average degree 13.36) given that we have 1000 of agents. The average link weight is -9143.91. 98% of all link weights are between -16000 and -1480:

##         1%        10%        20%        30%        50%        70% 
## -16004.864 -12997.479 -11814.849 -10957.176  -9448.605  -7733.493 
##        90%        99% 
##  -4526.232  -1480.070

0.999551 (almost all) of all links have negative or zero weight. A density plot of 98% of link weights (i.e. between -16000 and -1480) shows that they are more or less normally distributed:

## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

This shows that most of the links are positive but very small

Back to the experiment page

Date: Wed Feb 11 16:40:39 2015
Analysis version (challprop-analytics): {analysis_commitNo}
(use git checkout $commit$ if needed)

Back to the experiment page

Processes per agent

average number of elementary events per agent
eventsPerAgent
originalChallenges 19.97
challenged 33.48
interpreted 33.29
buffered 32.93
processed 29.99
penalized 33.29
benefitted 29.99
forgot 18.33
reinterpreted 29.99

Distribution of original challenges

## The number of original challenges in the simulation:  60100

Distribution of all challenges

## Total number of all challenges in the simulation:  100775

Distribution of processed challenges

## Total number of all processed challenges in the simulation:  90278

Back to the experiment page

Date: Thu Feb 5 11:45:15 2015
Analysis version (challprop-analytics): 56f6334
(use git checkout $commit$ if needed)

Back to the experiment page

Processes per agent

average number of elementary events per agent
eventsPerAgent
originalChallenges 21.60
challenged 647.19
interpreted 644.21
buffered 408.85
processed 139.29
penalized 644.21
benefitted 139.29
forgot 211.73
reinterpreted 139.29

Distribution of original challenges

The simulation goes this way: there are 10 thousand agents, 100 iterations. Each iteration 20% of agents get a fresh challenge generated by G-O-D, which is 2000 per iteration. These challenges in the beginning are (should be) distributed randomly across all agents in the network. If challenges are not distributed evenly across the network, that may cause buffers of agents lock some challenges and that would explain why we see somewhat low number of secondary propagation of challenges during further simulation. Lets see:

## The number of original challenges in the simulation:  21600

Distribution of all challenges

Looks like normal distribution again. Note that is different from [Analysis 3/2](/blog/2014/12/26/analysis-3-slash-2-distribution-of-original-challenges/) where the average degree of an agent was much lower:

## Total number of all challenges in the simulation:  647189

Distribution of processed challenges

## Total number of all processed challenges in the simulation:  139289

The number of processed challenges is almost identical for all agents. It is not surprising, because during each generation one agent can process only one challenge. This does not depend on how many challenges an agent gets. What is different, is that the more challenges an agent gets, the more ‘selection for relevance’ it performs.

A little discussion

Each original challenge is further propagated on average 29 times. Compare it to Experiment 3 where it is propagated on average 2.7 times.

Back to the experiment page